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1 function [obs, hidden] = mhmm_sample(T, numex, initial_prob, transmat, mu, Sigma, mixmat)
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2 % SAMPLE_MHMM Generate random sequences from an HMM with (mixtures of) Gaussian output.
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3 % [obs, hidden] = sample_mhmm(T, numex, initial_prob, transmat, mu, Sigma, mixmat)
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4 %
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5 % INPUTS:
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6 % T - length of each sequence
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7 % numex - num. sequences
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8 % init_state_prob(i) = Pr(Q(1) = i)
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9 % transmat(i,j) = Pr(Q(t+1)=j | Q(t)=i)
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10 % mu(:,j,k) = mean of Y(t) given Q(t)=j, M(t)=k
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11 % Sigma(:,:,j,k) = cov. of Y(t) given Q(t)=j, M(t)=k
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12 % mixmat(j,k) = Pr(M(t)=k | Q(t)=j) : set to ones(Q,1) or omit if single mixture
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13 %
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14 % OUTPUT:
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15 % obs(:,t,l) = observation vector at time t for sequence l
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16 % hidden(t,l) = the hidden state at time t for sequence l
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17
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18 Q = length(initial_prob);
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19 if nargin < 7, mixmat = ones(Q,1); end
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20 O = size(mu,1);
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21 hidden = zeros(T, numex);
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22 obs = zeros(O, T, numex);
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23
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24 hidden = mc_sample(initial_prob, transmat, T, numex)';
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25 for i=1:numex
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26 for t=1:T
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27 q = hidden(t,i);
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28 m = sample_discrete(mixmat(q,:), 1, 1);
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29 obs(:,t,i) = gaussian_sample(mu(:,q,m), Sigma(:,:,q,m), 1);
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30 end
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31 end
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